In the context of database systems, a “dimension” is a list of values that provide categories for data. A dimension acts as an index for identifying values of a variable. For example, if sales data has a separate sales figure for each month, then the data has a MONTH dimension. That is, the data is organized by month. A dimension is similar to a key in a relational database. Data that is organized by two or more dimensions is referred to as “multidimensional data”.
Any item of data within a multidimensional variable can be uniquely and completely selected by specifying one member from each of the variable's dimensions. For example, if a sales measure is dimensioned by MONTH, PRODUCT, and MARKET, specifying “January” for the MONTH dimension, “Stereos” for the PRODUCT dimension, and “Eastern Region” for the MARKET dimension uniquely specifies a single value of the measure. A multidimensional variable can be conceptually thought of as an N-dimensional array, where N is the number of dimensions of the variable, and where each value in the array may be accessed by specifying one dimension key value for each dimension (e.g. MDVar(dim1, dim2, dim3, . . . , dimN)). Thus, dimensions offer a concise and intuitive way of organizing and selecting data for retrieval, updating, and performing calculations.
Multidimensional arrays with 2 and 3 dimensions may be depicted visually as grids and cubes, respectively. For convenience, it has become customary to refer to the conceptual multidimensional arrays that correspond to multidimensional variables as “multidimensional cubes” (or simply “cubes”) regardless of how many dimensions they possess. Hence, a multidimensional data cube is a set of n-dimensional data objects. Further, each multidimensional value, or “measure” value, is said to belong to a “cell” of the cube, where the address of the cell is the set of dimension key values (one per dimension) that correspond to the multidimensional value contained therein.
For the purpose of explanation, the multidimensional value that belongs to a cell shall be referred to as the “cell value” of that cell. Cell values are associated with types of data, i.e., measures, on which a function is executed, such as a summation function, average function, minimum value function, maximum value function, and the like. For example, a cell might contain a value representing a summation of sales in dollars for a particular product in a particular time period in a particular market, where “sales” is the measure.
Multidimensional data may be stored in relational database systems (“RDBMS”) or in specialized, “multidimensional” database systems (“MDDBMS”). Multidimensional database systems provide structures and access techniques specifically designed for multidimensional data, and therefore provide relatively efficient storage and access to multidimensional data. However, when stored in specialized multidimensional database systems, only applications that are specially built to interact with those multidimensional database systems are able to access and manipulate the data.
On the other hand, when stored in relational database systems, all applications that support interaction with relational databases have access to the data. Such database applications communicate with the relational database system by submitting commands that conform to the database language supported by the relational database system, the most common of which is the ANSI Structured Query Language (SQL).
If the multidimensional data is stored in a multidimensional database, then accessing it via SQL without extracting the data and reinstating it as relational tables has traditionally been impossible. Hence, there is room for improvement in techniques for efficiently querying multidimensional data managed by a relational database management system. Specifically, there is room for improvement in techniques for accessing multidimensional data using the SQL.